In response to the 2009 H1N1 pandemic, interventions were conducted in an effort to limit transmission, including school closure, vaccination and antiviral prophylaxis. By and large, these approaches were meant to reduce the population level transmission of disease, rather than to protect specific individuals from infection. Using current methods, it is difficult to measure how effective such interventions were. Conducting a randomized controlled trial is difficult due to the unknown timing and location of outbreaks, as well as being ethically problematic. However, quantifying intervention effects is important. Knowing the extent to which transmission is reduced by particular control measures can help us to determine how many cases can be prevented by a particular approach, and whether the approach is sufficient to interrupt (rather than simply reduce) disease spread. Also, interventions are not without both direct and indirect social and financial costs. Deciding whether the effect of an intervention justifies its cost depends on the size of the expected effect and the severity of the disease being prevented. Finally, modern approaches to disease control and public health planning rely extensively on models of disease transmission. These models are only as good as their inputs, one of the most important of which is the effectiveness of any planned intervention. While mechanistic approaches to quantifying intervention effects may be useful, they are assumption laden and could be much improved by measures of the real world reduction in population level transmission provided by these interventions. The objective of the proposed research is to quantify the effects of interventions on the transmission dynamics of influenza through the development and validation of statistical techniques and enhanced study designs. Specifically, we aim to (1) estimate the effect of prophylactic use of antivirals combined with reactive vaccination on influenza transmission in long term care facilities, (2) estimate the effectiveness of social distancing measures to control influenza transmission in Thailand, and (3) develop and validate enhanced study designs for the investigation of outbreaks of influenza and other pathogens. The methods and techniques used to answer these questions will be widely applicable to other diseases (particularly respiratory viruses), and will provide researchers with new tools to investigate transmission dynamics and identify new methods of disease control. The proposed research is part of a K22 award. Prior to receiving the award the PI candidate will continue to work in his post-doctoral position under the supervision of Dr. Derek Cummings on research into the understanding and modeling of infectious disease. This research will be conducted under two grants, Immune landscapes of human influenza in households, towns and cities in southern China (NIH, 1 R01 TW 0008246-01) and the Vaccine Modeling Initiative (Bill and Melinda Gates Foundation). In his role in the Immune Landscapes project, the applicant will be involved in the collection of serological data on influenza infection from Guang Dong province, China, and the analysis of this data. This work will further his understanding of both the science and practicalities of infectious disease research. In the Vaccine Modeling Initiative, the applicant will work closely with Dr. Cummings and Dr. Bryan Grenfell in an analysis of the effectiveness of reactive vaccination in cholera outbreaks, thereby increasing his abilities in the modeling of infectious disease and the assessment of intervention effects. In addition, the candidate will continue to consult with his mentors Dr. Ron Brookmeyer (on statistical matters), and Dr. Charlotte Gaydos (on laboratory topics and virology). The experience and guidance he receives from this postdoctoral work will leave the candidate well prepared to both succeed in the proposed research and as an independent investigator. PUBLIC HEALTH RELEVENCE: An understanding of the dynamics of influenza transmission and the effectiveness of interventions in mitigating it is important due to the threat of emerging pandemic strains as wells as the high burden of endemic disease. This project aims to study the transmission dynamics of influenza and the effect of interventions through the development and application of statistical techniques and novel study designs. This research will provide a direct benefit to public health by estimating the effect of commonly used interventions and fundamental characteristics of influenza transmission.